https://agileanalytics.cloud/blog Latest news and articles from Agile Analytics development team en agile-analytics-cloud/web/scripts/generate-feed.js Thu, 27 Aug 2026 09:01:47 GMT https://agileanalytics.cloud/blog/dora-metrics-tools https://agileanalytics.cloud/blog/dora-metrics-tools Thu, 27 Aug 2026 09:01:47 GMT Every tool in this market will show you four numbers. The question worth asking is which of them it measured, and which of them it was told. Zoia Baletska https://agileanalytics.cloud/blog/dora-vs-space-which-engineering-metrics-framework-to-use https://agileanalytics.cloud/blog/dora-vs-space-which-engineering-metrics-framework-to-use Tue, 18 Aug 2026 09:38:14 GMT One is a fixed set of four delivery outcomes. The other is a way of choosing what to measure at all. Teams treat them as competing options and then wonder why the numbers do not add up. Zoia Baletska https://agileanalytics.cloud/blog/iteration-speed-what-fast-teams-actually-measure https://agileanalytics.cloud/blog/iteration-speed-what-fast-teams-actually-measure Fri, 14 Aug 2026 09:00:00 GMT The fastest iteration loops in the world right now are not in software. Understanding why they are fast says something uncomfortable about how most delivery teams work — and points at the handful of things worth measuring. Zoia Baletska https://agileanalytics.cloud/blog/pluralsight-flow-end-of-life-and-alternatives https://agileanalytics.cloud/blog/pluralsight-flow-end-of-life-and-alternatives Thu, 13 Aug 2026 08:26:23 GMT Renewals closed on 30 June 2026 and the product retires at the end of 2027. Appfire is not recommending a replacement, so here is an honest look at the options — including when the answer is not us. Arjan Franzen https://agileanalytics.cloud/blog/cycle-time-vs-lead-time-why-the-difference-matters https://agileanalytics.cloud/blog/cycle-time-vs-lead-time-why-the-difference-matters Thu, 18 Jun 2026 07:16:56 GMT Cycle time and lead time are often used interchangeably in Agile conversations, but they answer different questions — and mixing them up can create a lot of confusion. Cycle time measures how long it takes for work to be completed once someone starts working on it. Lead time measures how long it takes from the moment work is requested until it reaches production or the customer. That difference sounds small, but it changes the story entirely. Zoia Baletska https://agileanalytics.cloud/blog/agile-data-blind-spots-what-you-are-not-seeing-in-jira https://agileanalytics.cloud/blog/agile-data-blind-spots-what-you-are-not-seeing-in-jira Thu, 11 Jun 2026 07:32:47 GMT Discover hidden blockers, untracked work, and Jira data gaps that quietly distort Agile metrics and delivery insights. Zoia Baletska https://agileanalytics.cloud/blog/the-hidden-metrics-agile-teams-miss-and-why-velocity-alone-isnt-enough https://agileanalytics.cloud/blog/the-hidden-metrics-agile-teams-miss-and-why-velocity-alone-isnt-enough Thu, 04 Jun 2026 07:39:14 GMT Your sprint dashboard shows 85% of story points completed. The burndown chart looks healthy. But features still miss deadlines. Tech debt grows. Your team is exhausted. What's wrong? You have data everywhere — Jira is full of it, dashboards track everything, retrospectives are always scheduled. Yet somehow, you're drowning in visibility but starving for insight. Zoia Baletska https://agileanalytics.cloud/blog/flow-health-what-ticket-age-quietly-reveals-about-your-delivery-system https://agileanalytics.cloud/blog/flow-health-what-ticket-age-quietly-reveals-about-your-delivery-system Thu, 28 May 2026 07:06:59 GMT Most teams monitor throughput. They know how many tickets were completed this sprint, how many story points moved across the board, and how many releases happened last month. Those numbers are easy to surface and easy to compare over time. The problem is that they often describe output without saying much about the condition of the system producing it. Zoia Baletska https://agileanalytics.cloud/blog/5-practical-steps-to-deal-with-slow-release-cycles https://agileanalytics.cloud/blog/5-practical-steps-to-deal-with-slow-release-cycles Thu, 21 May 2026 07:37:08 GMT Most teams notice slow release cycles long before they can explain them. Features take longer to reach production than expected. Work sits in review queues for days. Releases become stressful enough that teams start avoiding them unless absolutely necessary. Eventually, delivery slows down to the point where planning becomes guesswork because nobody is confident about when work will actually ship. Zoia Baletska https://agileanalytics.cloud/blog/ai-observability-in-developer-workflows-what-the-latest-research-actually-shows https://agileanalytics.cloud/blog/ai-observability-in-developer-workflows-what-the-latest-research-actually-shows Thu, 14 May 2026 07:40:04 GMT AI has quietly settled into everyday development work. It shows up in code reviews, pull requests, documentation, and all the small decisions that keep things moving. What’s still missing, in many cases, is a clear understanding of what it’s actually doing there. A recent paper, AI Observability for Developer Productivity Tools: Bridging Cost Awareness and Code Quality, takes a closer look at that gap. Zoia Baletska https://agileanalytics.cloud/blog/agile-as-a-platform-how-it-slowly-gets-enshittified https://agileanalytics.cloud/blog/agile-as-a-platform-how-it-slowly-gets-enshittified Thu, 07 May 2026 07:11:03 GMT Agile was introduced as a way to reduce friction, not create new layers of it. Teams adopted it because it helped them move faster, make decisions closer to the work, and respond to change without waiting for permission. For a while, that promise held up. Delivery improved, collaboration felt more natural, and processes stayed lightweight enough to adjust when needed. Zoia Baletska https://agileanalytics.cloud/blog/platform-engineering-treating-your-platform-like-a-product https://agileanalytics.cloud/blog/platform-engineering-treating-your-platform-like-a-product Thu, 30 Apr 2026 07:13:47 GMT For a long time, internal platforms were treated as infrastructure. Something you built once, maintained quietly, and expected teams to adapt to. If things worked, nobody noticed. If they didn’t, developers found workarounds. That model doesn’t hold up anymore. Zoia Baletska https://agileanalytics.cloud/blog/embedded-vs-platform-vs-centralised-sre-which-model-actually-scales https://agileanalytics.cloud/blog/embedded-vs-platform-vs-centralised-sre-which-model-actually-scales Thu, 23 Apr 2026 07:08:40 GMT As systems grow, reliability stops being something a few engineers can “keep an eye on” and turns into a structural concern. Incidents become harder to trace, dependencies less obvious, and small failures start to cascade in ways that weren’t visible before. At that point, the question is no longer how to improve reliability, but how to organise for it. Zoia Baletska https://agileanalytics.cloud/blog/measuring-agileex-transforming-team-experience-into-actionable-insights-for-agile-success https://agileanalytics.cloud/blog/measuring-agileex-transforming-team-experience-into-actionable-insights-for-agile-success Thu, 16 Apr 2026 07:53:33 GMT Agile transformations rarely fail because teams don’t follow ceremonies. They fail quietly, over time, when the experience of working within the system becomes frustrating, slow, or disconnected from outcomes. That experience — how teams plan, build, release, and improve — is what we call AgileEx (Agile Experience). Zoia Baletska https://agileanalytics.cloud/blog/how-to-align-slos-with-user-experience-beyond-uptime-and-latency https://agileanalytics.cloud/blog/how-to-align-slos-with-user-experience-beyond-uptime-and-latency Thu, 09 Apr 2026 07:09:37 GMT Service Level Objectives are often presented as a way to bring clarity to reliability. In practice, many teams end up tracking what is easiest to measure rather than what is most meaningful. Uptime percentages and latency percentiles look precise on dashboards, yet they can give a false sense of confidence. A service can be “up” and still fail users in ways that matter—checkout flows that break halfway through, reports that arrive too late to be useful, or APIs that technically respond but return incomplete data. Zoia Baletska https://agileanalytics.cloud/blog/5-practical-steps-to-improve-software-delivery-without-adding-more-process https://agileanalytics.cloud/blog/5-practical-steps-to-improve-software-delivery-without-adding-more-process Thu, 02 Apr 2026 07:14:58 GMT Software delivery rarely fails because teams don’t work hard enough. More often, the issue is a lack of clarity: where time is going, what is slowing things down, and which changes actually improve outcomes. Zoia Baletska https://agileanalytics.cloud/blog/how-to-set-slos-that-developers-actually-respect https://agileanalytics.cloud/blog/how-to-set-slos-that-developers-actually-respect Thu, 26 Mar 2026 08:13:32 GMT Service Level Objectives (SLOs) have become a standard part of modern reliability engineering. They are supposed to help teams make informed decisions about reliability, prioritise work, and balance speed with stability. Yet in many organisations, SLOs exist only on paper. They live in dashboards that nobody checks or in documentation that nobody remembers writing. Developers continue shipping features, operations teams continue firefighting incidents, and the SLOs quietly drift out of relevance. Zoia Baletska https://agileanalytics.cloud/blog/slos-for-internal-services-what-to-track-when-you-dont-have-users https://agileanalytics.cloud/blog/slos-for-internal-services-what-to-track-when-you-dont-have-users Thu, 12 Mar 2026 08:41:05 GMT Service Level Objectives (SLOs) are a cornerstone of modern reliability engineering. They help teams understand, measure, and maintain the reliability of the services they build. For public-facing services, this is usually straightforward: uptime, latency, error rates, or user satisfaction can all serve as meaningful indicators. But what happens when your service doesn’t have external users? Internal microservices, background jobs, data pipelines, and internal business systems still power your operations — but traditional user metrics don’t exist. Zoia Baletska https://agileanalytics.cloud/blog/features-vs-non-features-ratio-understanding-your-thinking-pies https://agileanalytics.cloud/blog/features-vs-non-features-ratio-understanding-your-thinking-pies Thu, 05 Mar 2026 08:30:31 GMT In software development, not all work is created equal. The ratio of features to non-features — the features/non-features ratio, or what we sometimes call the “thinking pies” — can be surprisingly telling about how your team is spending its cognitive and operational bandwidth. Zoia Baletska https://agileanalytics.cloud/blog/slos-in-cloud-native-and-distributed-architectures https://agileanalytics.cloud/blog/slos-in-cloud-native-and-distributed-architectures Thu, 26 Feb 2026 09:48:22 GMT As organisations scale, their software platforms become increasingly distributed. Services are no longer isolated — they communicate, depend on each other, and often operate across different teams, cloud regions, and technology stacks. While this architecture improves scalability and resilience, it also complicates reliability management. Zoia Baletska https://agileanalytics.cloud/blog/slo-dashboards-that-tell-a-story-what-to-visualise-and-what-to-avoid https://agileanalytics.cloud/blog/slo-dashboards-that-tell-a-story-what-to-visualise-and-what-to-avoid Thu, 19 Feb 2026 09:07:41 GMT Most SLO dashboards answer a single question: “Did we meet the SLO?” Unfortunately, that’s also the least useful question you can ask. Great SLO dashboards don’t just report compliance. They tell a story — one that helps engineering, product, and leadership make better decisions. Zoia Baletska https://agileanalytics.cloud/blog/when-ai-doesnt-help-pitfalls-false-positives-and-how-to-detect-them-early https://agileanalytics.cloud/blog/when-ai-doesnt-help-pitfalls-false-positives-and-how-to-detect-them-early Thu, 12 Feb 2026 08:28:01 GMT n the previous articles in this series, we explored how to measure AI adoption, how to track output and quality, how AI affects developer experience, and how to combine those signals into a responsible AI impact dashboard. This article focuses on the less comfortable side of the story: what happens when AI doesn’t help — and how to detect that early, before damage accumulates. Zoia Baletska https://agileanalytics.cloud/blog/putting-it-all-together-how-to-build-an-ai-impact-dashboard-without-breaking-trust-or-teams https://agileanalytics.cloud/blog/putting-it-all-together-how-to-build-an-ai-impact-dashboard-without-breaking-trust-or-teams Thu, 05 Feb 2026 08:54:05 GMT By now, most software organisations have accepted a simple truth: AI is everywhere in development — whether leadership tracks it or not. What’s still missing in 2025 is not tooling, but coherence. Zoia Baletska https://agileanalytics.cloud/blog/devex-vs-space-how-to-measure-developer-experience-the-right-way https://agileanalytics.cloud/blog/devex-vs-space-how-to-measure-developer-experience-the-right-way Thu, 29 Jan 2026 09:01:24 GMT In the rapidly evolving world of software development, the old notion of “productivity = more code” is long dead. Modern engineering teams recognise that sustainable performance depends not just on output, but on the developer experience (DevEx) that underlies it. Two influential frameworks have emerged to help teams think about this holistically: SPACE and DevEx. Each offers a structured way to capture what matters — but they serve slightly different purposes and deliver different insights. Zoia Baletska https://agileanalytics.cloud/blog/developer-experience-and-long-term-health-measuring-ai-adoptions-impact-on-burnout-and-skill-growth https://agileanalytics.cloud/blog/developer-experience-and-long-term-health-measuring-ai-adoptions-impact-on-burnout-and-skill-growth Thu, 22 Jan 2026 08:43:24 GMT AI tools don’t just change how code is written — they change how developers think, learn, and experience their work. And those effects rarely show up in PR metrics or dashboards until it’s too late. This article focuses on Developer Experience (DevEx) and long-term team health — measuring whether AI adoption is sustainable over time, or quietly increasing burnout, cognitive load, and skill erosion beneath the surface. Zoia Baletska